Triple
T18070988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Done |
E432426
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Betfred betting shops |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Betfred betting shops | Statement: [Fred Done, associatedWith, Betfred betting shops]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betfred betting shops Context triple: [Fred Done, associatedWith, Betfred betting shops]
-
A.
Betfred
chosen
Betfred is a major UK-based bookmaker and online gambling company known for its extensive sports betting and gaming operations.
-
B.
Bet365
Bet365 is a major British online gambling company best known for its sports betting platform and global sponsorships in horse racing and football.
-
C.
Betfair
Betfair is a major online gambling company and betting exchange based in the United Kingdom.
-
D.
Betclic
Betclic is a European online sports betting and gambling company known for sponsoring major professional sports leagues and events.
-
E.
Hollywoodbets
Hollywoodbets is a South African-based sports betting and gaming company known for its extensive retail and online wagering services and prominent sports sponsorships.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ccee2fa4819098a61f7fdb8f1853 |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.